Abstract: 

The phenomenal volume and velocity of data generated in this digital world is exponentially increasing. Today, data collected across multiple feeds is heterogenous, complex and nonintuitive. The ultimate objective of collecting, storing and analyzing data at Walmart is to deliver value to customers, associates and our business. Value derived from data can be tangible, leading to increased sales and a higher return on investment, or intangible, leading to brand uplift and customer retention. “Productizing” data science is a journey that involves translation of insights obtained from exploratory analysis into scalable models that can power data products. This involves focusing on deploying models into production systems and effectively automating and scaling them. Productizing data science can have many benefits. It can help embed data science across all enterprise products and democratize its applications by placing it in the hands of even nontechnical business users. While productizing data science offers many benefits to organizations, it also presents numerous risks and challenges, like losing resource investments, risk of concept drift and risks of unintended consequences. This talk focuses on the nuances of productizing data science associated benefits and risks and also suggest strategies to overcome these risks.

Bio: 

Srujana is a Data Science & Analytics evangelist and advocate of using data science for social good. She has expertise in using applied intelligence techniques of machine learning for marketing & customer analytics. As a Director of Data Science & Value Realization at Walmart Labs, she leads data science portfolio to solve for unique business use cases to drive quantifiable data value. Srujana has held positions with Accenture, Google, and Hewlett Packard all in data science leadership capacities. She has had numerous articles in data science published in The Harvard Business Review (HBR) and INFORMS, among others. She earned her MBA in Operational Research, an Executive graduation in Analytics Strategy Management from Harvard University, and completed the Executive Program for Women Leaders at Stanford University. She is on the governing council of Analytical Society of India and cofounder of Women in Machine Learning and Data Science (WiMLDS) Bengaluru chapter. She is also a Women in Data Science (WiDS) ambassador. She enjoys fostering the communities of data science professionals and actively collaborating with United Nations Association (UNA) in attaining sustainable development goals through responsible usage of artificial intelligence technologies.